Assessing household financial burdens for preprimary education and associated socioeconomic inequalities: a case study in China

Background Providing young children with universal access to preprimary education (PPE) is considered a powerful tool for human capital development and eliminating the intergenerational transmission of poverty. To remove household financial barrier for achieving universal PPE, this study proposed a measure to identify households incurring ‘heavy financial burdens from paying for PPE’ (HBPPE) and conducted a case study in China. Methods Using nationally representative data in 2019, we estimated the percentage of households with HBPPE (spent 7% or more of their total annual expenditure) and associated socioeconomic inequalities. We also applied a three-level logit regression model to investigate the factors associated with the probabilities of households incurring HBPPE. Results Half of the sampled households spent 7% or more of their expenditures on PPE. Households in the lowest wealth quintile (54%) or households with children attending private PPE (55%) had higher percentages of HBPPE than households in other wealth quintiles (eg, 51% in the highest wealth quintile) or households with children attending public kindergartens (41%). Logit regression analysis shows that the poorest households and households with children attending private kindergarten were more likely to incur HBPPE than their counterparts. Conclusion To ensuring universal access to PPE in China, future policy should consider increasing the enrolment of children from low-income families in public kindergartens and increasing governmental investments in low-income households by subsidising children attending PPE.

. Government education spending on preschool education Figure A2. Preschool enrollment and gross enrollment rate Figure A3. Geographical distribution of the sampled counties Figure A4. Level and inequalities of PPE spending per child excluding out-school expenditure Figure A5. Level of % of households with HBPPE (threshold=10%) across 29 provinces Figure A6. Level of % of households with HBPPE (threshold=20%) across 29 provinces Figure A7. Level and inequalities of % of households with HBPPE (thresehold=10%) by type of kindergarten, maternal education, household net wealth quintile, and current residential area Figure A8. Level and inequalities of % of households with HBPPE (thresehold=20%) by type of kindergarten, maternal education, household net wealth quintile, and current residential area Figure A9. Level and inequalities of % of households with HBPPE (threshold=7%) by excluding out-school spending from PPE spending Figure A10. Level and inequalities of % of households with HBPPE (threshold=10%) by excluding out-school spending from PPE spending Figure A11. Level and inequalities of % of households with HBPPE (threshold=20%) by excluding out-school spending from PPE spending Figure A12. Level of % of household expenditure on PPE across 29 provinces Figure A13. Level and inequalities of % of household expenditure on PPE by type of kindergarten, maternal education, household net wealth quintile, and current residential area Figure A14. Level and inequalities of % of household expenditure on PPE by excluding outschool spending from PPE spending Table A1. Significance testing of inequalities in PPE spending per child using Cumming and Finch's "rule of eye" Table A2. Level of % of households with HBPPE (threshold=7%) across 29 provinces and weighted mean difference of the mean between the provincial level and the national level Table A3. Level of % of households with HBPPE (threshold=10%) across 29 provinces and weighted mean difference of the mean between the provincial level and the national level Table A4. Level of % of households with HBPPE (threshold=20%) across 29 provinces and weighted mean difference of the mean between the provincial level and the national level Table A5. Significance testing of inequalities in HBPPE (threshold=7%) using Cumming and Finch's "rule of eye" Table A6. Significance testing of inequalities in HBPPE (threshold=10%) using Cumming and Finch's "rule of eye" Table A7. Significance testing of inequalities in HBPPE (threshold=20%) using Cumming and Finch's "rule of eye" Table A8. Level of % of household expenditure on PPE across 29 provinces and weighted mean difference of the mean between the provincial level and the national level Table A9. Multilevel linear regression results on factors associated with logged % of household expenditure on PPE Table A10. Beta regression results on factors associated with % of household expenditure on PPE Table A11. Multilevel logit regression results on factors associated with household status on HBPPE by setting household net wealth as a continuous variable Table A12. Multilevel linear regression and Beta regression results on factors associated with % of household expenditure on PPE by setting household net wealth as a continuous variable

Chapter 1 Sampling Process of the China Household Finance Survey (CHFS)
The CHFS survey employs a stratified three-stage probability proportion-to-size random sample design. The primary sampling units include 2,585 counties/provincial cities in China. Tibet, Xinjiang, Hong Kong, Macao, and Chinese Taipei are not included in the primary sampling stage. The second stage of sampling involves selecting residential committees/villages from the counties/cities. The last stage involves selecting households from the residential committees/villages. A household unit is defined as all members of a housing unit related by blood, marriage, or some other legal arrangement; or two or more persons use incomes to make joint expenditures, or a single person living with others but is financially independent.
The CHFS is nationally and provincially representative survey conducted by the Southwestern University of Finance and Economics in China. 1 The CHFS collects rich information on household assets, income, wealth, and consumption. It also collects socioeconomic and demographic information on the sampled households and their members, including residential areas, household members' ages, gender, education levels, marital status, etc. The CHFS data are publicly available and have been widely used in the fields of economics, sociology, and education. By the end of November 2021, more than 35,000 researchers and 3,000 research institutions had registered and applied for the dataset, and the related studies have been published in top peer-reviewed journals. 2 Since 2017, the CHFS survey collaborated with the China Institute for Educational Finance Research and included a module on household spending on young children's care and education. The module includes questions on young children's schooling status, types of schooling, and household spending on various items that are related to preprimary attendance.
The CHFS 2019 covers 29 provinces (excluding Tibet, Xinjiang, Inner Mongolia, Hong Kong, and Macau), 345 cities/districts/counties, 34,643 households, and 107,008 individuals. Our focus was on household spending related to attending kindergarten. In China, kindergarten is usually offered to children 3-6 years old, but children under age 3 and over age 6 may also attend. In this study, we focused on children attending kindergarten, including those under age 3 or over age 6 BMJ Publishing Group Limited (BMJ) disclaims all liability and responsibility arising from any reliance Supplemental material placed on this supplemental material which has been supplied by the author(s) who still attended kindergarten (18.2%). The 2019 data includes 4,085 young children aged 3-6 years old and 2,797 of these children attended kindergartens, yielding a 68.5% net enrollment rate. 3 The number of children attending kindergarten in all age groups is 3,415, yielding 83.6% of the gross enrollment rate, very close to the governmental estimate (83.4%). 4 Among households in this dataset, 42 (0.18%) didn't report spending information on PPE, and two reported PPE spending exceeding total household expenditures, both of which groups were excluded. The final sample includes 3,371 young children attending kindergartens, from 3,111 households (9% of the total sample), who reported available PPE expenditure data. Springer. 2. Survey and Research Center for China Household Finance, at https://chfs.swufe.edu.cn/ 3. The net enrolment rate (NER) is the number of students of the age of preprimary education (age 3-6) that are enrolled in that level of education, expressed as a percentage of the total population in that age group. 4. The gross enrollment rate (GER) is the total enrollment in preprimary education, regardless of age, expressed as a percentage of the population in the official age group (age 3-6) corresponding to this level of education. The GER can exceed 100% because of early or late entry and/or grade repetition.

Chapter 2 Constructing variables on socioecnomic charisteristics
Household economic status. In this study, we used per capita household net wealth to measure household economic status. Household net wealth was derived by calculating the difference between a household's assets and its debts. Household total assets include financial assets (e.g., cash, deposit balances, social security, stock/bond value, etc.) and non-financial assets (e.g., agricultural capital, industrial capital, commercial capital, housing, vehicles, and durable goods). Total household debts include financial asset debts, agricultural debts, business debts, housing debts, vehicle debts, other non-financial asset debts, educational debts, credit card debts, medical-care debts, and other debts. In this study, we divided households into five quintiles according to their per capita net wealth. We examined the difference in % of households with HBPPE across the five wealth quintiles.
Maternal schooling. We divided maternal schooling into four categories: primary school or below, middle school, high school, and college or above. About 12% of households in the sample of the study did not report the mothers' education; we replaced the missing data with the predicted values of mother's education by regressing mothers' education on fathers' education (ordered logit regression coefficient = 1.217, p < 0.001; correlation coefficient = 0.746) or household heads' education (ordered logit regression coefficient = 1.393, p < 0.001; correlation coefficient = 0.632).
Residential area. Because the urban-rural divide is still one of the main structural problems facing the schooling system in China, we also examined the disparities between urban and rural residential areas. Type of kindergarten. Kindergartens in China can be categorized into public and private types, as discussed above. Households need to pay higher tuition fees to attend private kindergartens than public kindergartens. We assessed the disparities in the percentages of households with HBPPE by kindergarten type.

Chapter 3 Testing the statistical significance of inequalities in PPE spending and HBPPE using Cumming and Finch's "rule of eye"
To test whether inequalities in PPE spending or HBPPE significantly existed between the two sub-groups (e.g. male vs female), we compared the 95% confidence intervals (CIs) of the two estimates using Cumming and Finch's "rule of eye" for comparing two independent means. 1 We calculated three components that were required to implement the "rule of thumb": marginal errors, overlap of the 95% CIs of the two estimates, and proportion overlap. (1) Marginal error refers to the absolute difference between the mean and its lower or higher bound of a 95% CI. (2) An overlap of CIs refers to the difference between one estimate's higher bound and the other estimate's lower bound. (3) Proportion overlap is then calculated as the overlap of the 95% CIs between the two estimates divided by the average marginal errors between the two estimates.
Using Cumming and Finch's "rule of eye", we obtained proportion overlap using Equation PA1 or PA2 as follows, depending on the size of mean estimates for the two sub-groups. For example, when comparing the mean estimates for gender, if the mean estimate for male is lower than that for female, then = 95% −95% Or, if the mean estimate for male is higher than that for female, then = 95% −95% According to the "rule of eye", when sample sizes of two samples are both at least 10, and the margins of error between the two estimates do not differ by more than a factor of two, a proportion overlap less than 0.5 indicates a significant statistical relationship at the 0.05 level (i.e. p<0.05).

Chapter 4. Calculating provincial-level disparities in PPE spending and % of households wth HBPPE
Considering the large variation in economic development and geographical situations across the provinces, we further assessed the provincial-level disparities in the level of PPE spending per child and % of households with HBPPE across the 29 provinces. The information will help the central government to identify the provinces with high percentages of households with HBPPE. Provincial-level inequalities were estimated with the weighted mean from the national mean, which was calculated as the values of the mean difference between each province and the national average, weighted by each province's population share (= ×( − ) ), where pop is the national population size, popi is the population size of province i, r is the national mean, ri is the mean of province i.). The measures took zero as the reference, with positive values indicating provincial means higher than the national average, and negative values indicating provincial means lower than the national average.

Chapter 5. Sensitivity tests using proportion of households spending on PPE as the outcome variable and using continuous household net wealth as a predictor
We first calculated the provincial-level disparities in the % of household expenditure on PPE across the 29 provinces. On average, households in the Northeastern and middle areas spend higher % of household expenditure on PPE, with Shaanxi (15.4%) and Liaoning (14.8%) showing the highest level. Hebei (6.4%) and Gansu (6.0%) had the lowest level (Appendix Figure A12 and Table A8). The pattern across the provinces is similar to the % of households with HBPPE.
In terms of socioeconomic inequalities (Figure A13), % of expenditure on PPE was higher among those households sending children to private kindergartens (10.5%) than to public kindergartens (8.1%). Statistically significant differences were not observed across urban and rural households, households with different levels of maternal education, households with different wealth quintiles. The results are consistent with the ones using dichotomous HBPPE. Figure A14 shows results excluding out-school spending from PPE. The difference between private and public kindergartens increases. Meanwhile, the difference between the highest wealth quintile and the lowest one was statistically sigficant.
Tables A9 and A10 present the regression results using % of household total expenditure on PPE as outcome variable. We used both a multilevel linear regression and a beta regression for estimation. The results are consistent with the main regression results.
Tables A11 and A12 present the regression results using continuous household instead of net wealth quintiles as the predictor. We used a multilevel logit regression, multilevel linear regression and a beta regression for estimation. The results are consistent with the main regression results.  Notes: we used Cumming and Finch's "rule of eye" to test the statistical significance of inequalities in PPE spending between the two sub-groups (i.e. whether significant difference existed between the two mean estimates). Details for the analytical methods were presented in Appendix Panel A. Numbers in bold indicates a proportion overlap that is less than 0.5; that is a significant statistical relationship at the 0.05 level (p<0.05) BMJ Publishing Group Limited (BMJ) disclaims all liability and responsibility arising from any reliance Notes: we used Cumming and Finch's "rule of eye" to test the statistical significance of inequalities in HBPPE between the two subgroups (i.e. whether significant difference existed between the two mean estimates). Numbers in bold indicates a proportion overlap that is less than 0.5; that is a significant statistical relationship at the 0.05 level (p<0.05) BMJ Publishing Group Limited (BMJ) disclaims all liability and responsibility arising from any reliance 0.150 0.166 Notes: * p<0.10, ** p<0.05, *** p<0.01. We took the natural logarithm of % of household expenditure on PPE to ensure the variable close to a normal distribution. Of note, because 142 out of 3,111 observations reported zero value for this variable, taking log generated 142 missing values. We adopted a multilevel linear model for estimation. 2935 Notes: * p<0.10, ** p<0.05, *** p<0.01. We adopted a beta regression for estimation, which is appropritate for modelling proportional outcome variables that are greater than 0 and less than 1. Of note, because 142 out of 3,111 observations reported zero value for this variable, performing a beta regression would exclude these observations with zero values.